Getting Started with LIDAR Data

LIDAR, as I learned in my Geospatial Science Fundamentals course last Fall, is an active remote sensing technology that produces three dimensional representations of the earth's surface by gathering reflected laser light, usually from equipment mounted in an airplane passing over the modeled surface.  My Summer independent study at GMU will involve learning to process LIDAR data from the Northern Virginia region, with the aim of using the results to provide useful information to building owners in the region on the viability of putting solar panels on their roofs.

I will of course want to see if I can accomplish this task using free software, since citizen science is ill served by fees or licenses which restrict open collaboration on the widest possible scale.

I've been casually gathering information for the past few months, and here is what I've done so far:

• read about the US National Lidar Dataset project and found out Virginia's dataset status is listed as "partial".
• created an account on the USGS's EarthExplorer website.
• searched for Loudoun County Virginia and filtered on Lidar, which returned a single data set with LiDAR Entity ID: VA_LOUDOUNCO_2012_000298.
• downloaded the 76.7 MB "LAS Product" of this data.
• handed over the data to one of my students, Sam Phillips, who is helping me with this project.

Sam's blog is here. We plan to use our blogs to both document our work and have a digital dialog of our efforts. The impetus for this project comes from the GIS manager of the Northern Virginia Regional Commission, Shafi Khan, who came to our Code for NOVA meet up to propose the idea. I took notes from our first on the project, which included the following:

• The Minnesota Solar Suitability Analysis, a project of graduate students at the University of Minnesota, can serve as a model and resource for our project (see Minnesota LIDAR Solar Study for a YouTube video about the project).
• The Virginia Lidar website is a good resource for LIDAR data from Virginia.
• Harvard University Center for Geographic Analysis's WorldMap project could be a resource for making maps.

Finally, to provide structure to the theoretical study related to the project, I plan to make my way through Penn State University's GEOG 481: Topographic Mapping with Lidar, which wonderfully includes OpenCourseWare for the course.  I've ordered the required text book and will begin working on the lessons this week.

My next post on this topic will describe my first experiments using Python to process LIDAR data...